Overview

Dataset statistics

Number of variables29
Number of observations1696
Missing cells15654
Missing cells (%)31.8%
Duplicate rows6
Duplicate rows (%)0.4%
Total size in memory384.4 KiB
Average record size in memory232.1 B

Variable types

Categorical18
Numeric11

Alerts

Dataset has 6 (0.4%) duplicate rowsDuplicates
saturation is highly overall correlated with rr and 1 other fieldsHigh correlation
rr is highly overall correlated with saturationHigh correlation
sbp is highly overall correlated with dbpHigh correlation
dbp is highly overall correlated with sbpHigh correlation
hr is highly overall correlated with retractionsHigh correlation
temperature is highly overall correlated with fev1High correlation
fev1 is highly overall correlated with temperature and 14 other fieldsHigh correlation
charlson is highly overall correlated with cancerHigh correlation
death is highly overall correlated with fev1 and 1 other fieldsHigh correlation
death_aecopd is highly overall correlated with fev1 and 1 other fieldsHigh correlation
oedema is highly overall correlated with fev1High correlation
retractions is highly overall correlated with saturation and 3 other fieldsHigh correlation
confusion is highly overall correlated with fev1High correlation
dyspnoea_yesno is highly overall correlated with fev1 and 1 other fieldsHigh correlation
dyspnoea_mMRC is highly overall correlated with fev1 and 2 other fieldsHigh correlation
home_care is highly overall correlated with fev1High correlation
ami is highly overall correlated with fev1High correlation
heart_failure is highly overall correlated with fev1High correlation
cbd is highly overall correlated with fev1High correlation
pad is highly overall correlated with fev1High correlation
dementia is highly overall correlated with fev1High correlation
cancer is highly overall correlated with fev1 and 1 other fieldsHigh correlation
death is highly imbalanced (91.9%)Imbalance
death_aecopd is highly imbalanced (92.7%)Imbalance
confusion is highly imbalanced (94.3%)Imbalance
dyspnoea_yesno is highly imbalanced (63.1%)Imbalance
home_care is highly imbalanced (65.4%)Imbalance
ami is highly imbalanced (72.8%)Imbalance
heart_failure is highly imbalanced (64.2%)Imbalance
cbd is highly imbalanced (67.2%)Imbalance
pad is highly imbalanced (62.5%)Imbalance
dementia is highly imbalanced (89.3%)Imbalance
cancer is highly imbalanced (54.5%)Imbalance
saturation has 683 (40.3%) missing valuesMissing
rr has 1596 (94.1%) missing valuesMissing
sbp has 1302 (76.8%) missing valuesMissing
dbp has 1304 (76.9%) missing valuesMissing
hr has 1023 (60.3%) missing valuesMissing
temperature has 1433 (84.5%) missing valuesMissing
oedema has 1410 (83.1%) missing valuesMissing
retractions has 1551 (91.5%) missing valuesMissing
confusion has 479 (28.2%) missing valuesMissing
dyspnoea_yesno has 1017 (60.0%) missing valuesMissing
dyspnoea_mMRC has 1444 (85.1%) missing valuesMissing
fev1 has 1672 (98.6%) missing valuesMissing
bmi has 739 (43.6%) missing valuesMissing
aecopd_12m has 582 (34.3%) zerosZeros

Reproduction

Analysis started2023-04-03 19:29:25.591363
Analysis finished2023-04-03 19:29:43.680849
Duration18.09 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

death
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1679 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1679
99.0%
1 17
 
1.0%

Length

2023-04-03T15:29:43.750864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:43.899869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1679
99.0%
1 17
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1679
99.0%
1 17
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1679
99.0%
1 17
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1679
99.0%
1 17
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1679
99.0%
1 17
 
1.0%

death_aecopd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size13.4 KiB
0.0
1680 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5085
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1680
99.1%
1.0 15
 
0.9%
(Missing) 1
 
0.1%

Length

2023-04-03T15:29:43.987889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:44.104917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1680
99.1%
1.0 15
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 3375
66.4%
. 1695
33.3%
1 15
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3390
66.7%
Other Punctuation 1695
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3375
99.6%
1 15
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1695
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5085
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3375
66.4%
. 1695
33.3%
1 15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3375
66.4%
. 1695
33.3%
1 15
 
0.3%

sex
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
M
1419 
F
277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 1419
83.7%
F 277
 
16.3%

Length

2023-04-03T15:29:44.193999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:44.308025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
m 1419
83.7%
f 277
 
16.3%

Most occurring characters

ValueCountFrequency (%)
M 1419
83.7%
F 277
 
16.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1696
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1419
83.7%
F 277
 
16.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1696
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1419
83.7%
F 277
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 1419
83.7%
F 277
 
16.3%

season
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
winter
550 
autumn
520 
spring
390 
summer
236 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters10176
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowautumn
2nd rowautumn
3rd rowspring
4th rowsummer
5th rowwinter

Common Values

ValueCountFrequency (%)
winter 550
32.4%
autumn 520
30.7%
spring 390
23.0%
summer 236
13.9%

Length

2023-04-03T15:29:44.400551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:44.521578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
winter 550
32.4%
autumn 520
30.7%
spring 390
23.0%
summer 236
13.9%

Most occurring characters

ValueCountFrequency (%)
n 1460
14.3%
u 1276
12.5%
r 1176
11.6%
t 1070
10.5%
m 992
9.7%
i 940
9.2%
e 786
7.7%
s 626
6.2%
w 550
 
5.4%
a 520
 
5.1%
Other values (2) 780
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10176
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1460
14.3%
u 1276
12.5%
r 1176
11.6%
t 1070
10.5%
m 992
9.7%
i 940
9.2%
e 786
7.7%
s 626
6.2%
w 550
 
5.4%
a 520
 
5.1%
Other values (2) 780
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 10176
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1460
14.3%
u 1276
12.5%
r 1176
11.6%
t 1070
10.5%
m 992
9.7%
i 940
9.2%
e 786
7.7%
s 626
6.2%
w 550
 
5.4%
a 520
 
5.1%
Other values (2) 780
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1460
14.3%
u 1276
12.5%
r 1176
11.6%
t 1070
10.5%
m 992
9.7%
i 940
9.2%
e 786
7.7%
s 626
6.2%
w 550
 
5.4%
a 520
 
5.1%
Other values (2) 780
7.7%

aecopd_12m
Real number (ℝ)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.334316
Minimum0
Maximum8
Zeros582
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:44.617599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3682974
Coefficient of variation (CV)1.0254672
Kurtosis0.95238359
Mean1.334316
Median Absolute Deviation (MAD)1
Skewness1.0894001
Sum2263
Variance1.8722376
MonotonicityNot monotonic
2023-04-03T15:29:44.710620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 582
34.3%
1 483
28.5%
2 318
18.8%
3 180
 
10.6%
4 77
 
4.5%
5 44
 
2.6%
6 9
 
0.5%
7 2
 
0.1%
8 1
 
0.1%
ValueCountFrequency (%)
0 582
34.3%
1 483
28.5%
2 318
18.8%
3 180
 
10.6%
4 77
 
4.5%
5 44
 
2.6%
6 9
 
0.5%
7 2
 
0.1%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
7 2
 
0.1%
6 9
 
0.5%
5 44
 
2.6%
4 77
 
4.5%
3 180
 
10.6%
2 318
18.8%
1 483
28.5%
0 582
34.3%

saturation
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)3.3%
Missing683
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean91.650543
Minimum51
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:44.839650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile82
Q190
median93
Q395
95-th percentile97
Maximum100
Range49
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.3965562
Coefficient of variation (CV)0.058881879
Kurtosis7.8064187
Mean91.650543
Median Absolute Deviation (MAD)3
Skewness-2.1684894
Sum92842
Variance29.122818
MonotonicityNot monotonic
2023-04-03T15:29:44.955440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
95 125
 
7.4%
93 111
 
6.5%
94 111
 
6.5%
96 92
 
5.4%
90 81
 
4.8%
92 81
 
4.8%
97 72
 
4.2%
91 65
 
3.8%
88 36
 
2.1%
87 35
 
2.1%
Other values (23) 204
 
12.0%
(Missing) 683
40.3%
ValueCountFrequency (%)
51 1
 
0.1%
60 1
 
0.1%
62 1
 
0.1%
64 1
 
0.1%
68 3
 
0.2%
70 4
0.2%
72 1
 
0.1%
74 5
0.3%
75 8
0.5%
76 2
 
0.1%
ValueCountFrequency (%)
100 1
 
0.1%
99 11
 
0.6%
98 32
 
1.9%
97 72
4.2%
96 92
5.4%
95 125
7.4%
94 111
6.5%
93 111
6.5%
92 81
4.8%
91 65
3.8%

rr
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)19.0%
Missing1596
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean20.45
Minimum12
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:45.489253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q115
median20
Q324
95-th percentile36
Maximum40
Range28
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.0972679
Coefficient of variation (CV)0.34705467
Kurtosis0.093187579
Mean20.45
Median Absolute Deviation (MAD)5
Skewness0.87877336
Sum2045
Variance50.371212
MonotonicityNot monotonic
2023-04-03T15:29:45.631286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20 13
 
0.8%
24 11
 
0.6%
15 10
 
0.6%
13 9
 
0.5%
12 8
 
0.5%
18 8
 
0.5%
14 7
 
0.4%
16 6
 
0.4%
28 6
 
0.4%
36 4
 
0.2%
Other values (9) 18
 
1.1%
(Missing) 1596
94.1%
ValueCountFrequency (%)
12 8
0.5%
13 9
0.5%
14 7
0.4%
15 10
0.6%
16 6
0.4%
18 8
0.5%
20 13
0.8%
21 1
 
0.1%
22 4
 
0.2%
23 1
 
0.1%
ValueCountFrequency (%)
40 2
 
0.1%
36 4
 
0.2%
34 1
 
0.1%
32 3
 
0.2%
30 3
 
0.2%
28 6
0.4%
26 1
 
0.1%
25 2
 
0.1%
24 11
0.6%
23 1
 
0.1%

sbp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)21.3%
Missing1302
Missing (%)76.8%
Infinite0
Infinite (%)0.0%
Mean133.21574
Minimum74
Maximum210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:45.765316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile100
Q1120
median134.5
Q3145
95-th percentile166.05
Maximum210
Range136
Interquartile range (IQR)25

Descriptive statistics

Standard deviation20.350908
Coefficient of variation (CV)0.15276655
Kurtosis0.55249298
Mean133.21574
Median Absolute Deviation (MAD)14.5
Skewness0.15214932
Sum52487
Variance414.15945
MonotonicityNot monotonic
2023-04-03T15:29:45.911357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 42
 
2.5%
140 33
 
1.9%
150 23
 
1.4%
130 23
 
1.4%
135 16
 
0.9%
110 14
 
0.8%
100 12
 
0.7%
145 11
 
0.6%
125 9
 
0.5%
136 8
 
0.5%
Other values (74) 203
 
12.0%
(Missing) 1302
76.8%
ValueCountFrequency (%)
74 1
 
0.1%
77 1
 
0.1%
80 1
 
0.1%
88 1
 
0.1%
90 4
0.2%
92 1
 
0.1%
93 1
 
0.1%
94 1
 
0.1%
95 1
 
0.1%
96 2
0.1%
ValueCountFrequency (%)
210 1
 
0.1%
190 2
 
0.1%
186 3
0.2%
183 1
 
0.1%
181 1
 
0.1%
180 2
 
0.1%
176 1
 
0.1%
173 1
 
0.1%
172 2
 
0.1%
170 5
0.3%

dbp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct55
Distinct (%)14.0%
Missing1304
Missing (%)76.9%
Infinite0
Infinite (%)0.0%
Mean72.229592
Minimum30
Maximum103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:46.053879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile54
Q164
median70
Q380
95-th percentile93
Maximum103
Range73
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.114895
Coefficient of variation (CV)0.16772759
Kurtosis-0.097599569
Mean72.229592
Median Absolute Deviation (MAD)10
Skewness0.023025
Sum28314
Variance146.77068
MonotonicityNot monotonic
2023-04-03T15:29:46.183909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 48
 
2.8%
70 47
 
2.8%
60 42
 
2.5%
75 19
 
1.1%
85 16
 
0.9%
65 14
 
0.8%
67 13
 
0.8%
90 11
 
0.6%
66 11
 
0.6%
64 9
 
0.5%
Other values (45) 162
 
9.6%
(Missing) 1304
76.9%
ValueCountFrequency (%)
30 1
 
0.1%
40 1
 
0.1%
44 2
 
0.1%
46 2
 
0.1%
47 1
 
0.1%
48 3
0.2%
50 6
0.4%
51 1
 
0.1%
52 2
 
0.1%
54 4
0.2%
ValueCountFrequency (%)
103 1
 
0.1%
102 1
 
0.1%
100 3
 
0.2%
99 1
 
0.1%
98 2
 
0.1%
96 1
 
0.1%
95 9
0.5%
94 1
 
0.1%
93 4
0.2%
92 2
 
0.1%

hr
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct86
Distinct (%)12.8%
Missing1023
Missing (%)60.3%
Infinite0
Infinite (%)0.0%
Mean84.647845
Minimum38
Maximum155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:46.319939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile59
Q172
median84
Q395
95-th percentile114
Maximum155
Range117
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.189141
Coefficient of variation (CV)0.20306649
Kurtosis0.49755739
Mean84.647845
Median Absolute Deviation (MAD)11
Skewness0.44888826
Sum56968
Variance295.46658
MonotonicityNot monotonic
2023-04-03T15:29:46.447968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 29
 
1.7%
78 24
 
1.4%
90 23
 
1.4%
82 22
 
1.3%
100 22
 
1.3%
88 19
 
1.1%
75 19
 
1.1%
72 19
 
1.1%
84 17
 
1.0%
85 17
 
1.0%
Other values (76) 462
27.2%
(Missing) 1023
60.3%
ValueCountFrequency (%)
38 1
 
0.1%
42 1
 
0.1%
47 1
 
0.1%
49 1
 
0.1%
50 6
0.4%
51 1
 
0.1%
52 1
 
0.1%
54 1
 
0.1%
55 5
0.3%
56 8
0.5%
ValueCountFrequency (%)
155 1
0.1%
150 1
0.1%
138 2
0.1%
137 1
0.1%
133 1
0.1%
132 1
0.1%
131 1
0.1%
128 1
0.1%
127 1
0.1%
126 1
0.1%

temperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)16.3%
Missing1433
Missing (%)84.5%
Infinite0
Infinite (%)0.0%
Mean295.85551
Minimum35
Maximum394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:46.589411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile36
Q1353
median362
Q3371
95-th percentile383
Maximum394
Range359
Interquartile range (IQR)18

Descriptive statistics

Standard deviation135.59173
Coefficient of variation (CV)0.45830387
Kurtosis-0.027432419
Mean295.85551
Median Absolute Deviation (MAD)9
Skewness-1.39586
Sum77810
Variance18385.116
MonotonicityNot monotonic
2023-04-03T15:29:46.710437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
36 33
 
1.9%
365 20
 
1.2%
362 14
 
0.8%
355 13
 
0.8%
363 11
 
0.6%
37 10
 
0.6%
364 10
 
0.6%
35 9
 
0.5%
354 9
 
0.5%
366 8
 
0.5%
Other values (33) 126
 
7.4%
(Missing) 1433
84.5%
ValueCountFrequency (%)
35 9
 
0.5%
36 33
1.9%
37 10
 
0.6%
38 3
 
0.2%
39 1
 
0.1%
341 1
 
0.1%
347 1
 
0.1%
351 2
 
0.1%
352 4
 
0.2%
353 5
 
0.3%
ValueCountFrequency (%)
394 1
 
0.1%
391 1
 
0.1%
389 2
0.1%
388 3
0.2%
386 2
0.1%
385 3
0.2%
383 3
0.2%
382 1
 
0.1%
381 1
 
0.1%
379 2
0.1%

oedema
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.7%
Missing1410
Missing (%)83.1%
Memory size13.4 KiB
0.0
193 
1.0
93 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 193
 
11.4%
1.0 93
 
5.5%
(Missing) 1410
83.1%

Length

2023-04-03T15:29:46.834465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:46.934487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 193
67.5%
1.0 93
32.5%

Most occurring characters

ValueCountFrequency (%)
0 479
55.8%
. 286
33.3%
1 93
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 572
66.7%
Other Punctuation 286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 479
83.7%
1 93
 
16.3%
Other Punctuation
ValueCountFrequency (%)
. 286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 479
55.8%
. 286
33.3%
1 93
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 479
55.8%
. 286
33.3%
1 93
 
10.8%

retractions
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.4%
Missing1551
Missing (%)91.5%
Memory size13.4 KiB
0.0
99 
1.0
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters435
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 99
 
5.8%
1.0 46
 
2.7%
(Missing) 1551
91.5%

Length

2023-04-03T15:29:47.021507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:47.144534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 99
68.3%
1.0 46
31.7%

Most occurring characters

ValueCountFrequency (%)
0 244
56.1%
. 145
33.3%
1 46
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 290
66.7%
Other Punctuation 145
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 244
84.1%
1 46
 
15.9%
Other Punctuation
ValueCountFrequency (%)
. 145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 435
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 244
56.1%
. 145
33.3%
1 46
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 244
56.1%
. 145
33.3%
1 46
 
10.6%

confusion
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing479
Missing (%)28.2%
Memory size13.4 KiB
0.0
1209 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3651
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1209
71.3%
1.0 8
 
0.5%
(Missing) 479
 
28.2%

Length

2023-04-03T15:29:47.238555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:47.345114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1209
99.3%
1.0 8
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 2426
66.4%
. 1217
33.3%
1 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2434
66.7%
Other Punctuation 1217
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2426
99.7%
1 8
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2426
66.4%
. 1217
33.3%
1 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2426
66.4%
. 1217
33.3%
1 8
 
0.2%

dyspnoea_yesno
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing1017
Missing (%)60.0%
Memory size13.4 KiB
1.0
631 
0.0
 
48

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2037
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 631
37.2%
0.0 48
 
2.8%
(Missing) 1017
60.0%

Length

2023-04-03T15:29:47.437135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:47.545158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 631
92.9%
0.0 48
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 727
35.7%
. 679
33.3%
1 631
31.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1358
66.7%
Other Punctuation 679
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 727
53.5%
1 631
46.5%
Other Punctuation
ValueCountFrequency (%)
. 679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 727
35.7%
. 679
33.3%
1 631
31.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 727
35.7%
. 679
33.3%
1 631
31.0%

dyspnoea_mMRC
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)2.0%
Missing1444
Missing (%)85.1%
Memory size13.4 KiB
4.0
113 
0.0
48 
3.0
42 
2.0
38 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters756
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row0.0
4th row0.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 113
 
6.7%
0.0 48
 
2.8%
3.0 42
 
2.5%
2.0 38
 
2.2%
1.0 11
 
0.6%
(Missing) 1444
85.1%

Length

2023-04-03T15:29:47.633179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:47.746204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 113
44.8%
0.0 48
19.0%
3.0 42
 
16.7%
2.0 38
 
15.1%
1.0 11
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 300
39.7%
. 252
33.3%
4 113
 
14.9%
3 42
 
5.6%
2 38
 
5.0%
1 11
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 504
66.7%
Other Punctuation 252
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 300
59.5%
4 113
 
22.4%
3 42
 
8.3%
2 38
 
7.5%
1 11
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 756
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 300
39.7%
. 252
33.3%
4 113
 
14.9%
3 42
 
5.6%
2 38
 
5.0%
1 11
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 300
39.7%
. 252
33.3%
4 113
 
14.9%
3 42
 
5.6%
2 38
 
5.0%
1 11
 
1.5%

age
Real number (ℝ)

Distinct56
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.683962
Minimum40
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:47.874235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile57
Q169
median78
Q383
95-th percentile89
Maximum100
Range60
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.9687004
Coefficient of variation (CV)0.13171483
Kurtosis-0.0053090932
Mean75.683962
Median Absolute Deviation (MAD)6
Skewness-0.61185665
Sum128360
Variance99.374987
MonotonicityNot monotonic
2023-04-03T15:29:48.002264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 93
 
5.5%
80 90
 
5.3%
82 81
 
4.8%
83 78
 
4.6%
79 78
 
4.6%
85 69
 
4.1%
84 66
 
3.9%
76 65
 
3.8%
77 63
 
3.7%
86 60
 
3.5%
Other values (46) 953
56.2%
ValueCountFrequency (%)
40 1
 
0.1%
45 2
 
0.1%
46 3
 
0.2%
47 4
 
0.2%
48 2
 
0.1%
49 3
 
0.2%
50 6
0.4%
51 10
0.6%
52 6
0.4%
53 10
0.6%
ValueCountFrequency (%)
100 2
 
0.1%
98 1
 
0.1%
97 5
 
0.3%
96 3
 
0.2%
95 5
 
0.3%
94 6
 
0.4%
93 11
0.6%
92 11
0.6%
91 10
0.6%
90 19
1.1%

fev1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)83.3%
Missing1672
Missing (%)98.6%
Infinite0
Infinite (%)0.0%
Mean1035.3333
Minimum36
Maximum6773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:48.120290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile40.45
Q152.75
median67
Q3645.25
95-th percentile5664.95
Maximum6773
Range6737
Interquartile range (IQR)592.5

Descriptive statistics

Standard deviation1992.5061
Coefficient of variation (CV)1.9245068
Kurtosis3.1861863
Mean1035.3333
Median Absolute Deviation (MAD)15.5
Skewness2.0692122
Sum24848
Variance3970080.5
MonotonicityNot monotonic
2023-04-03T15:29:48.223314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
69 4
 
0.2%
2478 2
 
0.1%
56 1
 
0.1%
4911 1
 
0.1%
48 1
 
0.1%
6773 1
 
0.1%
59 1
 
0.1%
36 1
 
0.1%
54 1
 
0.1%
568 1
 
0.1%
Other values (10) 10
 
0.6%
(Missing) 1672
98.6%
ValueCountFrequency (%)
36 1
0.1%
40 1
0.1%
43 1
0.1%
48 1
0.1%
51 1
0.1%
52 1
0.1%
53 1
0.1%
54 1
0.1%
56 1
0.1%
57 1
0.1%
ValueCountFrequency (%)
6773 1
 
0.1%
5798 1
 
0.1%
4911 1
 
0.1%
2478 2
0.1%
877 1
 
0.1%
568 1
 
0.1%
75 1
 
0.1%
69 4
0.2%
65 1
 
0.1%
59 1
 
0.1%

bmi
Real number (ℝ)

Distinct536
Distinct (%)56.0%
Missing739
Missing (%)43.6%
Infinite0
Infinite (%)0.0%
Mean2633.1536
Minimum25
Maximum5039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:48.356343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile286.8
Q12446
median2773
Q33116
95-th percentile3651.2
Maximum5039
Range5014
Interquartile range (IQR)670

Descriptive statistics

Standard deviation868.16666
Coefficient of variation (CV)0.32970605
Kurtosis2.5090386
Mean2633.1536
Median Absolute Deviation (MAD)329
Skewness-1.4868126
Sum2519928
Variance753713.35
MonotonicityNot monotonic
2023-04-03T15:29:48.496380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2873 10
 
0.6%
2564 8
 
0.5%
3223 7
 
0.4%
301 6
 
0.4%
2748 6
 
0.4%
2747 6
 
0.4%
2474 6
 
0.4%
2838 6
 
0.4%
2597 6
 
0.4%
2799 5
 
0.3%
Other values (526) 891
52.5%
(Missing) 739
43.6%
ValueCountFrequency (%)
25 1
 
0.1%
28 1
 
0.1%
30 1
 
0.1%
32 1
 
0.1%
35 3
0.2%
156 1
 
0.1%
162 1
 
0.1%
207 1
 
0.1%
221 2
0.1%
222 1
 
0.1%
ValueCountFrequency (%)
5039 1
0.1%
5031 1
0.1%
4602 1
0.1%
4417 1
0.1%
4316 1
0.1%
4291 1
0.1%
4272 1
0.1%
4179 1
0.1%
4175 1
0.1%
4134 1
0.1%

rural
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
923 
1
773 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 923
54.4%
1 773
45.6%

Length

2023-04-03T15:29:48.617404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:48.725428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 923
54.4%
1 773
45.6%

Most occurring characters

ValueCountFrequency (%)
0 923
54.4%
1 773
45.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 923
54.4%
1 773
45.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 923
54.4%
1 773
45.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 923
54.4%
1 773
45.6%

home_care
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1586 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1586
93.5%
1 110
 
6.5%

Length

2023-04-03T15:29:48.818449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:48.926473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1586
93.5%
1 110
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 1586
93.5%
1 110
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1586
93.5%
1 110
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1586
93.5%
1 110
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1586
93.5%
1 110
 
6.5%

charlson
Real number (ℝ)

Distinct12
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2464623
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-04-03T15:29:49.010492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9841524
Coefficient of variation (CV)0.88323423
Kurtosis3.818171
Mean2.2464623
Median Absolute Deviation (MAD)0
Skewness2.0766383
Sum3810
Variance3.9368606
MonotonicityNot monotonic
2023-04-03T15:29:49.103513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 854
50.4%
2 439
25.9%
3 142
 
8.4%
7 82
 
4.8%
4 74
 
4.4%
8 56
 
3.3%
5 25
 
1.5%
6 8
 
0.5%
9 8
 
0.5%
12 4
 
0.2%
Other values (2) 4
 
0.2%
ValueCountFrequency (%)
1 854
50.4%
2 439
25.9%
3 142
 
8.4%
4 74
 
4.4%
5 25
 
1.5%
6 8
 
0.5%
7 82
 
4.8%
8 56
 
3.3%
9 8
 
0.5%
10 3
 
0.2%
ValueCountFrequency (%)
12 4
 
0.2%
11 1
 
0.1%
10 3
 
0.2%
9 8
 
0.5%
8 56
 
3.3%
7 82
4.8%
6 8
 
0.5%
5 25
 
1.5%
4 74
4.4%
3 142
8.4%

ami
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1617 
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1617
95.3%
1 79
 
4.7%

Length

2023-04-03T15:29:49.202535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:49.309559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1617
95.3%
1 79
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 1617
95.3%
1 79
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1617
95.3%
1 79
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1617
95.3%
1 79
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1617
95.3%
1 79
 
4.7%

heart_failure
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1581 
1
 
115

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1581
93.2%
1 115
 
6.8%

Length

2023-04-03T15:29:49.399579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:49.507605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1581
93.2%
1 115
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 1581
93.2%
1 115
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1581
93.2%
1 115
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1581
93.2%
1 115
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1581
93.2%
1 115
 
6.8%

cbd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1594 
1
 
102

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1594
94.0%
1 102
 
6.0%

Length

2023-04-03T15:29:49.597631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:49.703649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1594
94.0%
1 102
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 1594
94.0%
1 102
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1594
94.0%
1 102
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1594
94.0%
1 102
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1594
94.0%
1 102
 
6.0%

pad
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1573 
1
 
123

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1573
92.7%
1 123
 
7.3%

Length

2023-04-03T15:29:49.790669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:49.900693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1573
92.7%
1 123
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 1573
92.7%
1 123
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1573
92.7%
1 123
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1573
92.7%
1 123
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1573
92.7%
1 123
 
7.3%

dementia
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1672 
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1672
98.6%
1 24
 
1.4%

Length

2023-04-03T15:29:49.986712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:50.093736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1672
98.6%
1 24
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 1672
98.6%
1 24
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1672
98.6%
1 24
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1672
98.6%
1 24
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1672
98.6%
1 24
 
1.4%

diabetes
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1296 
1
400 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1296
76.4%
1 400
 
23.6%

Length

2023-04-03T15:29:50.181756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:50.289783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1296
76.4%
1 400
 
23.6%

Most occurring characters

ValueCountFrequency (%)
0 1296
76.4%
1 400
 
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1296
76.4%
1 400
 
23.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1296
76.4%
1 400
 
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1296
76.4%
1 400
 
23.6%

cancer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1534 
1
162 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1534
90.4%
1 162
 
9.6%

Length

2023-04-03T15:29:50.377803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:29:50.484826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1534
90.4%
1 162
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 1534
90.4%
1 162
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1534
90.4%
1 162
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1534
90.4%
1 162
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1534
90.4%
1 162
 
9.6%

Interactions

2023-04-03T15:29:41.414897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:28.042894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:29.285220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:31.635891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:32.719993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:33.959782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:35.088599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:36.303938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:37.854704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:39.101986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:40.173095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:41.530926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:28.167822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:29.409251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:31.744917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:32.834018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:34.070806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:35.207773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:36.416964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:37.978732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:39.205876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:40.324128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:41.639702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:28.280634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:29.517277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:31.866948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:32.939042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:34.175210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:35.332801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:36.535833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:38.091756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:39.303898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:40.453645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:41.736726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:28.375655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:29.621300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:31.970949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:33.044065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:34.272232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:35.436824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:36.634856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:38.194779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:39.387917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:40.550249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:41.857751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:28.486786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:29.727842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:32.058968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:33.218104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:34.380257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:35.544848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:36.739385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:38.302804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:39.482939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:40.648271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:41.971780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:28.593809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:29.833866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:32.159991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:33.333129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:34.488464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:35.655873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:36.850410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:38.412829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:39.567957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:40.749294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:42.109841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:28.700838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:29.951125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:32.252012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:33.439413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:34.590487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:35.766897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:36.965436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:38.534856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:39.669980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:40.868320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:42.225868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:28.805113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:30.063152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:32.350908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:33.552438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:34.694510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:35.876245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:37.092464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:38.648884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:39.765007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:40.985346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:42.340894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:28.921138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:31.290872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:32.453933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:33.659461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:34.799534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:35.986269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:37.198488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:38.765908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:39.861022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:41.091370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:42.444917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:29.024163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:31.392837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:32.539952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:33.754736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:34.886553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:36.089293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:37.643623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:38.871932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:39.968049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:41.194393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:42.559942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:29.157192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:31.512864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:32.620970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:33.853758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:34.985575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:36.190315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:37.742339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:38.985960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:40.070071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:29:41.304873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-03T15:29:50.598121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
aecopd_12msaturationrrsbpdbphrtemperatureagefev1bmicharlsondeathdeath_aecopdsexseasonoedemaretractionsconfusiondyspnoea_yesnodyspnoea_mMRCruralhome_careamiheart_failurecbdpaddementiadiabetescancer
aecopd_12m1.000-0.041-0.099-0.068-0.0850.006-0.0640.115-0.187-0.0720.0660.1010.0770.0650.0780.0000.0000.0000.0000.0000.0000.1130.0000.0530.0000.0000.0000.0370.035
saturation-0.0411.000-0.532-0.0710.007-0.234-0.129-0.1010.2770.008-0.0840.3590.3930.0000.0510.0000.5360.2240.0500.1090.0160.0380.0000.0860.1030.0000.0000.0680.000
rr-0.099-0.5321.0000.138-0.0370.3140.0550.013NaN0.091-0.2560.2720.3550.0000.0000.2290.4560.0000.0000.2130.0000.4120.0000.0000.1030.0000.1840.0000.213
sbp-0.068-0.0710.1381.0000.5300.052-0.081-0.0760.2050.140-0.0830.1040.1270.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.1550.0000.1820.1150.0000.000
dbp-0.0850.007-0.0370.5301.0000.171-0.115-0.2510.3590.207-0.1690.1380.1370.0000.0000.1340.3480.0000.0000.0000.0250.0710.0840.1520.0000.1360.2450.0970.000
hr0.006-0.2340.3140.0520.1711.0000.136-0.176-0.2290.028-0.0410.1870.1990.0130.0000.2020.5750.2950.0000.0000.0000.0000.0000.0000.0000.0000.1460.0420.000
temperature-0.064-0.1290.055-0.081-0.1150.1361.000-0.1351.0000.0680.0220.0000.0000.0000.0000.0850.0000.0610.0700.0000.0000.0000.0380.0440.0250.0350.1570.0000.072
age0.115-0.1010.013-0.076-0.251-0.176-0.1351.0000.231-0.1660.1280.0000.0000.1690.0740.1960.0820.0000.0000.0000.0910.3650.0540.1790.1120.1260.0770.0510.104
fev1-0.1870.277NaN0.2050.359-0.2291.0000.2311.000-0.0880.0841.0001.0000.0000.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.1851.000
bmi-0.0720.0080.0910.1400.2070.0280.068-0.166-0.0881.0000.0280.0000.0000.1830.0000.2390.2040.0580.0000.0000.0280.0480.0160.0460.0890.0000.0950.1930.086
charlson0.066-0.084-0.256-0.083-0.169-0.0410.0220.1280.0840.0281.0000.0350.0000.0540.0000.0860.0000.0000.1210.1200.0350.1250.3120.3240.2760.3240.2240.3770.932
death0.1010.3590.2720.1040.1380.1870.0000.0001.0000.0000.0351.0000.9350.0000.0000.0000.0000.1260.0000.1020.0220.0000.0410.0500.0000.0000.0000.0000.000
death_aecopd0.0770.3930.3550.1270.1370.1990.0000.0001.0000.0000.0000.9351.0000.0000.0000.0000.0000.0290.0000.0780.0000.0000.0480.0570.0000.0000.0000.0000.000
sex0.0650.0000.0000.0000.0000.0130.0000.1690.0000.1830.0540.0000.0001.0000.0030.0770.0000.0000.0000.0000.0290.0260.0000.0000.0250.0990.0250.0080.012
season0.0780.0510.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.0031.0000.1700.2570.0000.0060.0900.0000.0350.0550.0000.0140.0000.0340.0620.000
oedema0.0000.0000.2290.0000.1340.2020.0850.1961.0000.2390.0860.0000.0000.0770.1701.0000.2890.1040.0250.1730.0000.2070.0650.0650.0000.0000.0000.0000.000
retractions0.0000.5360.4560.0000.3480.5750.0000.0821.0000.2040.0000.0000.0000.0000.2570.2891.0000.0000.2250.6600.0920.0000.0000.0000.0000.0000.0000.0000.000
confusion0.0000.2240.0000.0000.0000.2950.0610.0001.0000.0580.0000.1260.0290.0000.0000.1040.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
dyspnoea_yesno0.0000.0500.0000.0000.0000.0000.0700.0001.0000.0000.1210.0000.0000.0000.0060.0250.2250.0001.0000.9940.0220.0000.0000.0000.0750.0000.0000.0000.083
dyspnoea_mMRC0.0000.1090.2130.0760.0000.0000.0000.0001.0000.0000.1200.1020.0780.0000.0900.1730.6600.0000.9941.0000.1650.0780.0930.0610.1500.0000.0000.0000.068
rural0.0000.0160.0000.0000.0250.0000.0000.0910.0000.0280.0350.0220.0000.0290.0000.0000.0920.0000.0220.1651.0000.0090.0340.1350.0000.0000.0260.0040.000
home_care0.1130.0380.4120.0000.0710.0000.0000.3651.0000.0480.1250.0000.0000.0260.0350.2070.0000.0000.0000.0780.0091.0000.0300.0630.0970.0850.0000.0480.002
ami0.0000.0000.0000.0000.0840.0000.0380.0541.0000.0160.3120.0410.0480.0000.0550.0650.0000.0000.0000.0930.0340.0301.0000.0390.0000.0170.0000.0000.000
heart_failure0.0530.0860.0000.1550.1520.0000.0440.1791.0000.0460.3240.0500.0570.0000.0000.0650.0000.0000.0000.0610.1350.0630.0391.0000.0000.0400.0730.0260.046
cbd0.0000.1030.1030.0000.0000.0000.0250.1121.0000.0890.2760.0000.0000.0250.0140.0000.0000.0000.0750.1500.0000.0970.0000.0001.0000.0170.0360.0290.032
pad0.0000.0000.0000.1820.1360.0000.0350.1261.0000.0000.3240.0000.0000.0990.0000.0000.0000.0000.0000.0000.0000.0850.0170.0400.0171.0000.0000.0380.000
dementia0.0000.0000.1840.1150.2450.1460.1570.0771.0000.0950.2240.0000.0000.0250.0340.0000.0000.0000.0000.0000.0260.0000.0000.0730.0360.0001.0000.0230.018
diabetes0.0370.0680.0000.0000.0970.0420.0000.0510.1850.1930.3770.0000.0000.0080.0620.0000.0000.0000.0000.0000.0040.0480.0000.0260.0290.0380.0231.0000.000
cancer0.0350.0000.2130.0000.0000.0000.0720.1041.0000.0860.9320.0000.0000.0120.0000.0000.0000.0000.0830.0680.0000.0020.0000.0460.0320.0000.0180.0001.000

Missing values

2023-04-03T15:29:42.764988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-03T15:29:43.187718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-03T15:29:43.480822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

deathdeath_aecopdsexseasonaecopd_12msaturationrrsbpdbphrtemperatureoedemaretractionsconfusiondyspnoea_yesnodyspnoea_mMRCagefev1bmiruralhome_carecharlsonamiheart_failurecbdpaddementiadiabetescancer
000.0Mautumn095.0NaNNaNNaN55.0NaNNaNNaNNaNNaNNaN73NaN3621.01010000000
100.0Mautumn192.026.0124.062.069.035.01.00.00.01.02.085NaN2917.00040000110
200.0Mspring097.0NaNNaNNaN67.0NaNNaNNaN0.01.0NaN84NaN2917.00040000110
300.0Msummer0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN81NaNNaN0080000011
400.0Mwinter2NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN61NaNNaN0030000010
500.0Mautumn297.0NaNNaNNaN67.0NaNNaNNaN0.01.0NaN87NaNNaN0010000000
600.0Mautumn086.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaN76NaN3389.00020100000
700.0Mspring096.013.0NaNNaNNaNNaN0.00.00.0NaNNaN71NaN2917.00010000000
800.0Mautumn2NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN72NaN2558.00010000000
900.0Mspring199.0NaNNaNNaN71.0NaNNaNNaN0.01.0NaN71NaN2558.00010000000
deathdeath_aecopdsexseasonaecopd_12msaturationrrsbpdbphrtemperatureoedemaretractionsconfusiondyspnoea_yesnodyspnoea_mMRCagefev1bmiruralhome_carecharlsonamiheart_failurecbdpaddementiadiabetescancer
168600.0Mautumn093.0NaNNaNNaNNaN359.00.00.00.01.0NaN81NaNNaN0110000000
168700.0Mautumn197.0NaNNaNNaN76.0NaNNaNNaN0.01.0NaN97NaN3906.00110000000
168800.0Mautumn196.0NaN121.082.091.0352.00.0NaN0.0NaNNaN76NaN2922.00010000000
168900.0Msummer095.0NaNNaNNaN84.0NaNNaNNaN0.0NaNNaN76NaN2922.00010000000
169000.0Mautumn292.0NaN190.070.091.0365.00.0NaN0.01.04.080NaN1931.00010000000
169100.0Mwinter196.0NaN181.087.094.0359.0NaNNaN0.01.03.079NaN1931.00010000000
169200.0Mautumn092.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaN78NaN3258.00070000001
169300.0Mwinter095.0NaNNaNNaN89.0NaNNaNNaN0.01.0NaN70NaN2723.00031001000
169411.0Mwinter2NaN24.0110.067.056.0375.01.0NaN0.01.0NaN84NaNNaN1020100000
169511.0Mspring392.0NaN100.060.0100.038.00.0NaN0.01.04.085NaN3225.01020100000

Duplicate rows

Most frequently occurring

deathdeath_aecopdsexseasonaecopd_12msaturationrrsbpdbphrtemperatureoedemaretractionsconfusiondyspnoea_yesnodyspnoea_mMRCagefev1bmiruralhome_carecharlsonamiheart_failurecbdpaddementiadiabetescancer# duplicates
400.0Mwinter0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN79NaNNaN10100000003
000.0Mautumn0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN78NaNNaN00200000102
100.0Mautumn2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN83NaNNaN00100000002
200.0Mautumn5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN75NaNNaN00100000002
300.0Mspring3NaNNaNNaNNaNNaNNaNNaNNaN0.01.0NaN82NaN2474.000200000102
500.0Mwinter3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN87NaN2751.000201000002